The Analytics Centre of Excellence (ACOE) in IQVIA is positively impacting patient lives through the anticipation and delivery of Decision Intelligence solutions that increase clinical trial success, shorten drug development timelines and reduce costs in bringing new drugs to market, getting much-needed drugs to patients faster through successful clinical trial delivery.

Our vision at the ACOE is that every decision our users and clients make in R&D is made through Decision Intelligence, allowing speedy access to safe, novel, and effective treatments for all patients.
 

ACOE Product Portfolio

In trial strategy, we are using Machine Learning (ML) to recommend countries and clinical trial sites and accurately predict clinical study timelines. We are deploying ML at clinical trial sites to read Electronic Medical Records data and find undiagnosed patients that are otherwise challenging to identify. We are optimizing our patient outreach targeting and are predicting participant dropout. In addition, problems in patient recruitment are being solved with ML. Further upstream in R&D we are predicting clinical trial outcomes, drug-protein interactions, repurposing drugs and even leveraging ML to optimize molecules.

 

Job Overview:

• Develop fit for purpose AIML models/algorithms/processes to address pharma/healthcare applications and innovative products upon completion of prototypes followed by the building of production grade algorithms/automation engines for client deliverables.
• Test for viability in order to deliver final products to clients.
• Able to bring newly researched ideas to reality quickly and on a large scale.
• Design, build, test, and deliver products from post-prototype to client delivery.

 

Essential Functions:

• Lead the transformation of machine learning research domain expertise in the areas of human data into viable prototypes.
• Lead the development of features of models on individual projects and/or products with guidance and support from others.
• Develop understanding of the creation of new algorithms through working alongside other Machine Learning Engineers and Machine Learning Research Scientists
• Lead the building and training of new production grade algorithms that can learn from complex, high dimensionality data in order to uncover patterns from which machine learning models and applications can be developed.
• Use a variety of techniques in order to improve the performance of individual natural language processing and/or machine learning algorithms.
• Lead the testing and validation of models to determine viability for deployment with guidance and support from others.
• Lead the creation of fit for purpose experiments and prototype ideas to address pharma questions.
• Consult for internal and external clients, leads solution development and innovation for complex proposals, leads client AI project technical delivery.

 

Typical Managerial Activities

• Administratively manage employees, conduct performance reviews including development plans, make staffing decisions.
• Maintain up-to-date knowledge of machine learning techniques, including software development approaches and methodologies.
• Lead improvements in data science and machine learning best practices.
• Keep team members informed of company processes, priorities and general information.
• Create effective onboarding plans to ensure minimum time to value.
• Responsible for value proposition of the development team, ensuring team members are aligned with their role and contribution.
• Identify unmet skill requirements and hire or develop staff to satisfy them.
• The manager will also act as coach and mentor not only to their team but other machine learning engineers and data scientists in the ACOE and wider IQVIA, providing guidance, support, and opportunities for growth and development.

 

Essential Requirements:

• STEM-related degree (Bachelor’s, Master’s or Doctorate)
• Substantial experience working on creating machine learning algorithms
• Prior engineering project leadership using relevant skills and technologies: Python (Scikit-learn, Pandas, Numpy, Scipy) SQL, linux/mac command-line tools.
• Used Deep Neural Network libraries such as Tensor Flow or Pytorch.
• Experience with building, testing, measuring, and deploying machine learning models in production.
• Familiarity with ML algorithms (classification, regression) and processes (how to build models, assess their goodness of fit, etc).
• Familiarity with agile software development lifecycle (SCRUM, Kanban, etc.).
• Previous experience of owning, maintaining and enhancing software data products.
• Attention to clarity of code, ease of development, and correctness of implementations.
• Good knowledge of software development best practices including testing, continuous integration, and MLOps tools.
• Experience working with large, real-world datasets.
• Experience with mentoring and training junior team members, especially pair programming.

 

Preferred Requirements:

• Knowledge of biostatistics/life sciences/healthcare technology.
• Experience with deploying ML applications to production.
• Experience with supporting and maintaining ML applications in production.
• Experience with clinical domain and with regulated data.
• Knowledge of cloud systems such as AWS, Azure, GCP and containerisation such as Docker.
• Demonstrated in-depth understanding of product development lifecycle.
• Demonstrated aptitude for and interest in peer mentorship.
• Experience deploying code into production through CI/CD tools.
• Knowledge of UX principles.
• Experience working in the Hadoop ecosystem.
• Prior management training preferred but not required.